Electronic Arts

As a Data Engineer, you will be involved in the entire development lifecycle, from brainstorming ideas to implementing scalable solutions that unlock data insights. You will collaborate with stakeholders to gather requirements, design data models, and build pipelines that support reporting, analytics, and exploratory analysis.

Key Responsibilities:

• Design, build, and sustain efficient, scalable and performant Data Engineering Pipelines to ingest, sanitize, transform (ETL/ELT), and deliver high-volume, high-velocity data from diverse sources. 

• Ensure reliable and consistent processing of versatile workloads of granularity such as Real Time, Near Real Time, Mini-batch, Batch and On-demand.

• Translate business requirements into technical specifications, design and implement solutions with clear documentation.

• Develop, optimize, and support production data workflows to ensure comprehensive and accurate datasets.

• With Software Engineering mindset/discipline, adopt best practices in writing modular code that is maintainable, scalable and performant.

• Use orchestration and scheduling tools (e.g., Airflow, GitLab Runners) to streamline workflows. 

• Automate deployment and monitoring of data workflows using CI/CD best practices. Use DevOps best practices to instrument retries and self-healing to minimize, if not avoid manual intervention.

• Use AI/Gen AI tools and technologies to build/generate reusable code.

• Collaborate with Architects, Data scientists, BI engineers, Analysts, Product/ Program Mgmt and other stakeholders to deliver end-to-end solutions.

• Promote strategies to improve our data modelling, quality and architecture

• Mentor junior engineers, and contribute to team knowledge sharing.

• Have an analytics mindset to explore and identify opportunities for improved metrics, insights contributing to business growth.

Qualifications:

• Masters or Bachelors degree in Computer science or associated discipline with relevant industry experience(3+ Years) in a data engineering role.

• Strong Proficiency in writing and analyzing complex SQL, Python or any 4GL.

• Strong experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (e.g., Terraform, CloudFormation).

• Strong hands-on experience with Cloud Data Warehouses like Snowflake, Redshift, BigQuery or other big data solutions

• Expertise & Experience with Data Lake and Open table formats technologies (e.g., Apache Iceberg)

• Expertise & Experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Beam, Trino).

• Experience with real-time/streaming data technologies (e.g., Kafka, Kinesis, Spark Streaming).

• Experience in using modern pipeline orchestration tools such as Airflow

• Knowledge in  data warehousing concepts, data modelling and performance optimization techniques.

• Experience in version control and CI/CD workflows (e.g., Git).

• Familiarity with BI Tools like Looker, Tableau & Power BI, dashboard design, data quality, data governance, and data observability tools 

• Experience with containerization (Docker, Kubernetes).

• Experience working in Agile development environments and with tools like JIRA or Confluence.

• Strong problem-solving, analytical, and debugging skills.

• Excellent communication and collaboration skills, with the ability to work across business, analytics, and engineering teams.

Desired:

• Exposure to machine learning pipelines, MLOps, or AI-driven data products.

• Familiarity with AI/ML concepts and effective collaboration with data science or AI/ML teams.

• Experience integrating data solutions with AI/ML platforms or supporting AI-driven analytics at scale.